Imaging and Deconvolution David J' Wilner HarvardSmithsonian CfA

1
Imaging and DeconvolutionDavid J.
Wilner(Harvard-Smithsonian CfA)

• HIA (Sub)Millimeter Observing Techniques Summer
  School
• August 14-17, 2006

2
References

• Thompson, A.R., Moran, J.M. Swensen, G.W. 1986,
  Interferometry and Synthesis in Radio Astronomy
  (New York Wiley) also 2nd edition
• NRAO Summer School proceedings
• http//www.aoc.nrao.edu/events/synthesis/
• Perley, R.A., Schwab, F.R. Bridle, A.H., eds.
  1989, ASP Conf. Series 6, Synthesis Imaging in
  Radio Astronomy (San Francisco ASP)
• Chapter 6 Imaging (Sramek Schwab), Chapter 8
  Deconvolution (Cornwell)
• Lecture by T. Cornwell 2002, Imaging and
  Deconvolution
• Lecture by S. Bhatnagar 2004, Imaging and
  Deconvolution
• IRAM Summer School proceedings
• http//www.iram.fr/IRAMFR/IS/archive.html
• Guilloteau, S., ed. 2000, IRAM Millimeter
  Interferometry Summer School
• Chapter 13 Imaging Principles, Chapter 16
  Imaging in Practice (Guilloteau)
• Lecture by J. Pety 2004 Imaging, Deconvolution
  Image Analysis
• Talks by many practitioners (Chandler, Wright,
  Welch, Downes, )

3
Visibility and Sky Brightness

• from the van Citttert-Zernike theorem (TMS
  Appendix 3.1)
• for small fields of view
  
  the complex visibility,V(u,v)
  
  is the 2D Fourier transform of
  
  the brightness on the sky,T(x,y)
• u,v (wavelengths) are spatial frequencies in
  E-W
  and N-W directions, i.e. the baseline lengths
• x,y (rad) are angles in tangent plane relative to
  a
  reference position in the E-W and N-S directions

y
x
T(x,y)
4
The Fourier Transform

• Fourier theory states that any signal (here
  images) can be expressed as
  a sum of sinusoids
• (x,y) plane and (u,v) plane are conjugate
• T(x,y)
  V(u,v) FTT(x,y)
• in this example a single Fourier component
  encodes all
• the spatial frequency period of the wave
• the magnitude contrast
• the phase (not shown) shift of wave with
  respect to origin
• Fourier Transform image contains all information
  of original image

5
The Fourier Domain

• acquire comfort with the Fourier domain
• In older texts, functions and their Fourier
  transforms occupy upper and lower domains, as if
  functions circulated at ground level and their
  transforms in the underworld (Bracewell 1965)
• a few properties of the Fourier transform
• scaling
• shifting
• convolution/multiplication
• sampling theorem

6
Some 2D Fourier Transform Pairs

• T(x,y) AmpV(u,v)

Gaussian Gaussian ? Function
Constant narrow features transform to wide
features (and vice-versa)
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More 2D Fourier Transform Pairs

• T(x,y) AmpV(u,v)

Disk Bessel sharp edges
result in many high spatial frequencies Ell.
Gaussian Ell. Gaussian orientations are
orthogonal in the (x,y) and (u,v) planes
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2D Fourier Transform Pairs

• T(x,y) AmpV(u,v)

structure on many scales

• T(x,y) is real, but V(u,v) is complex (in
  general)
• Real and Imaginary
• Amplitude and Phase
• Amplitude tells how much of a certain frequency
  component, Phase tells where
• V(-u,-v) V(u,v) where is complex
  conjugation (Hermitian)
• V(u0,v0) ? integral of T(x,y)dxdy total flux

9
Visibility and Sky Brightness
10
Visibility and Sky Brightness
11
Aperture Synthesis

• sample V(u,v) at enough points to synthesis the
  equivalent large aperture of size (umax,vmax)
• 1 pair of telescopes ? 1 (u,v) sample at a time
• N telescopes ? number of samples N(N-1)/2
• reconfigure physical layout of N telescopes for
  more
• fill in (u,v) plane by making use of Earth
  rotation (Sir Martin Ryle,
  1974 Nobel Prize in Physics)

3 configurations of 8 SMA antennas at 345 GHz
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Aperture Synthesis Telescopes
CARMA (OVROBIMA)
(e)VLA
13
Imaging (u,v) plane Sampling

• in aperture synthesis, V(u,v) samples are limited
  by number of telescopes, and Earth-sky geometry

• high spatial frequencies
• maximum angular resolution
• low spatial frequencies
• extended structures invisible
• irregular within high/low limits
• sampling theorem violated
• information lost

14
Formal Description

• sample Fourier domain at discrete points
• the inverse Fourier transform is
• the convolution theorem tells us
• where (the
  point spread function)
• Fourier transform of sampled visibilities yields
  the true sky brightness convolved with the point
  spread function
• (the dirty image is the true image convolved
  with the dirty beam)

15
Dirty Beam and Dirty Image
b(x,y) (dirty beam)
B(u,v)
TD(x,y) (dirty image)
T(x,y)
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How to analyze interferometer data?

• uv plane analysis
• best for simple sources, e.g. point sources,
  disks
• image plane analysis
• Fourier transform V(u,v) samples to image plane,
  get TD(x,y)
• but difficult to do science on dirty image
• deconvolve b(x,y) from TD(x,y) to determine
  (model of) T(x,y)

visibilities ? dirty image ? sky
brightness
Fourier transform deconvolve
17
Details of the Dirty Image

• Fourier Transform
• Fast Fourier Transform (FFT) much faster than
  simple Fourier summation, O(NlogN) for 2N x 2N
  image
• FFT requires data on regularly spaced grid
• aperture synthesis observations not on a regular
  grid
• Gridding is used to resample V(u,v) for FFT
• customary to use a convolution technique
• visibilities are noisy samples of a smooth
  function
• nearby visibilities not independent
• use special (Spheroidal) functions with nice
  properties
• fall off quickly in (u,v) plane (not too much
  smoothing)
• fall off quickly in image plane (avoid aliasing)

18
Primary Beam
T(x,y)

• A telescope does not have uniform response across
  the entire sky
• main lobe approximately Gaussian, fwhm 1.2?/D
  (where D is ant diameter) primary beam
• limited field of view
• sidelobes, error beam (usually not important)
• primary beam response modifies sky brightness
  T(x,y) ? A(x,y)T(x,y)
• correct with division by A(x,y) in image plane

A(x,y)
SMA 345 GHz
ALMA 690 GHz
T(x,y) large A(x,y)
small A(x,y)
19
Pixel Size and Image Size

• pixel size
• should satisfy sampling theorem for the longest
  baselines, ?x lt 1/2 umax , ?y lt 1/2 vmax
• in practice, 3 to 5 pixels across the main lobe
  of the dirty beam (to aid deconvolution)
• image size
• natural resolution in (u,v) plane samples
  FTA(x,y), implies image size 2x primary beam
• if there are bright sources in the sidelobes of
  A(x,y), then they will be aliased into the image
  (need to make a larger image)

20
Dirty Beam Shape and N Antennas

• 2 Antennas

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Dirty Beam Shape and N Antennas

• 3 Antennas

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Dirty Beam Shape and N Antennas

• 4 Antennas

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Dirty Beam Shape and N Antennas

• 5 Antennas

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Dirty Beam Shape and N Antennas

• 6 Antennas

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Dirty Beam Shape and N Antennas

• 7 Antennas

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Dirty Beam Shape and N Antennas

• 8 Antennas

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Dirty Beam Shape and Super Synthesis

• 8 Antennas x 2 samples

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Dirty Beam Shape and Super Synthesis

• 8 Antennas x 6 samples

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Dirty Beam Shape and Super Synthesis

• 8 Antennas x 30 samples

30
Dirty Beam Shape and Super Synthesis

• 8 Antennas x 107 samples

31
Dirty Beam Shape and Weighting

• introduce weighting function W(u,v)
• W modifies sidelobes of dirty beam
• W is also gridded for FFT
• Natural weighting
• W(u,v) 1/?2(u,v) at points with data and
  zero elsewhere where ?2(u,v) is the noise
  variance of the (u,v) sample
• maximizes point source sensitivity
  (lowest rms in image)
• gives more weight to shorter baselines (larger
  spatial scales), degrades resolution

32
Dirty Beam Shape and Weighting

• Uniform weighting
• W(u,v) is inversely proportional to local density
  of (u,v) points, so sum of weights in a (u,v)
  cell is a constant (or zero)
• fills (u,v) plane more uniformly, so
  (outer) sidelobes are lower
• gives more weight to long baselines and therefore
  higher angular resolution
• degrades point source sensitivity
  (higher rms in image)
• can be trouble with sparse sampling (cells
  with few data points have same weight as cells
  with many data points)

33
Dirty Beam Shape and Weighting

• Robust (Briggs) weighting
• variant of uniform that avoids giving too much
  weight to cell with low natural weight
• implementations differ, e.g. SN is natural weight
  of a cell, St is a threshold
• large threshold? natural weighting
• small threshold ? uniform weighting
• parameter allows continuous variation between
  optimal angular resolution and optimal point
  source sensitivity

34
Dirty Beam Shape and Weighting

• Tapering
• apodize the (u,v) sampling by a Gaussian
• t tapering parameter (in k? arcsec)
• like smoothing in the image plane (convolution by
  a Gaussian)
• gives more weight to shorter baselines, degrades
  angular resolution
• degrades point source sensitivity but can improve
  sensitivity to extended structure
• could use an elliptical Gaussian
• limits to usefulness

35
Weighting and Tapering Noise
Natural 1.7x1.4 ?1.0
Robust 1.0x0.8 ?1.28
Uniform 0.9x0.7 ?1.58
Robust Taper 1.7x1.4 ?1.30
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Weighting and Tapering Summary

• imaging parameters provide a lot of freedom

37
Deconvolution

• difficult to do science on dirty image
• deconvolve b(x,y) from TD(x,y) to recover T(x,y)
• information is missing, so be careful!

dirty image
CLEAN image
38
Deconvolution Philosophy

• to keep you awake at night
• ? an infinite number of T(x,y) compatible with
  sampled V(u,v), i.e. invisible distributions
  R(x,y) where b(x,y) ? R(x,y) 0
• no data beyond umax,vmax ? unresolved structure
• no data within umin,vmin ? limit on largest size
  scale
• holes between umin,vmin and umax,vmax ? sidelobes
• noise ? undetected/corrupted structure in T(x,y)
• no unique prescription for extracting optimum
  estimate of true sky brightness from visibility
  data
• deconvolution
• uses non-linear techniques effectively
  interpolate/extrapolate samples of V(u,v) into
  unsampled regions of the (u,v) plane
• aims to find a sensible model of T(x,y)
  compatible with data
• requires a priori assumptions about T(x,y)

39
Deconvolution Algorithms

• most common algorithms in radio astronomy
• CLEAN (Högbom 1974)
• a priori assumption T(x,y) is a collection of
  point sources
• variants for computational efficiency, extended
  structure
• Maximum Entropy (Gull and Skilling 1983)
• a priori assumption T(x,y) is smooth and
  positive
• vast literature about the deep meaning of entropy
  (Bayesian)
• hybrid approaches of these can be effective
• deconvolution requires knowledge of beam shape
  and image noise properties (usually OK for
  aperture synthesis)
• atmospheric seeing can modify effective beam
  shape
• deconvolution process can modify image noise
  properties

40
Basic CLEAN Algorithm

• Initialize
• a residual map to the dirty map
• a Clean component list to empty
• Identify strongest feature in residual map as a
  point source
• Add a fraction g (the loop gain) of this point
  source to the clean component list
• Subtract the fraction g times b(x,y) from
  residual map
• If stopping criteria not reached, goto step 2 (an
  interation)
• Convolve Clean component (cc) list by an estimate
  of the main lobe of the dirty beam (the Clean
  beam) and add residual map to make the final
  restored image

b(x,y)
TD(x,y)
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Basic CLEAN Algorithm (cont)

• stopping criteria
• residual map max lt multiple of rms (when noise
  limited)
• residual map max lt fraction of dirty map max
  (dynamic range limited)
• max number of clean components reached
• loop gain good results for g 0.1 to 0.3
• easy to include a priori information about where
  to search for clean components (clean boxes)
• very useful but potentially dangerous!
• Schwarz (1978) CLEAN is equivalent to a least
  squares fit of sinusoids (in the absense of noise)

42
CLEAN
CLEAN model
TD(x,y)
restored image
residual map
43
CLEAN with Box
CLEAN model
TD(x,y)
restored image
residual map
44
CLEAN with Poor Choice of Box
CLEAN model
TD(x,y)
restored image
residual map
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CLEAN Variants

• Clark CLEAN
• aims at faster speed for large images
• Högbom-like minor cycle w/ truncated dirty
  beam, subset of largest residuals
• in major cycle, ccs are FFTd and subtracted
  from the FFT of the residual image from the
  previous major cycle
• Cotton-Schwab CLEAN (MX)
• in major cycle, ccs are FFTd and subtracted
  from ungridded visibilities
• more accurate but slower (gridding steps
  repeated)
• Steer, Dewdny, Ito (SDI) CLEAN
• aims to supress CLEAN stripes in smooth,
  extended emission
• in minor cycles, any point in the residual map
  greater than a fraction (lt1) of the maximum is
  taken as a cc
• Multi-Resolution CLEAN
• aims to account for coupling between pixels by
  extended structure
• independently CLEAN a smooth map and a difference
  map, fewer ccs

46
Restored Images

• CLEAN beam size
• natural choice is to fit the central peak of the
  dirty beam with elliptical Gaussian
• unit of deconvolved map is Jy per CLEAN beam area
  ( intensity, can convert to
  brightness temperature)
• minimize unit problems when adding dirty map
  residuals
• modest super resolution often OK, but be careful
• restored image does not fit the visibility data
• photometry should be done with caution
• CLEAN does not conserve flux (extrapolates)
• extended structure missed, attenuated, distorted
• phase errors (e.g. seeing) can spread signal
  around

47
Noise in Images

• point source sensitivity straightforward
• telescope area, bandwidth, integration time,
  weighting
• in image, modify noise by primary beam response
• extended source sensitivity problematic
• not quite right to divide noise by ?n beams
  covered by source smoothing tapering,
  omitting data ? lower limit
• always missing flux at some spatial scale
• be careful with low signal-to-noise images
• if position known, 3? OK for point source
  detection
• if position unknown, then 5? required (flux
  biased by 1?)
• if lt 6?, cannot measure the source size (require
  3? difference between long and short
  baselines)
• spectral lines may have unknown position,
  velocity, width

48
Maximum Entropy Algorithm

• Maximize a measure of smoothness (the entropy)
• subject to the constraints
• M is the default image
• fast (NlogN) non-linear optimization solver due
  to Cornwell and Evans (1983)
• optional convolve with Gaussian beam and add
  residual map to make map

b(x,y)
TD(x,y)
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Maximum Entropy Algorithm (cont)

• easy to include a priori information with default
  image
• flat default best only if nothing known (or
  nothing observed!)
• straightforward to generalize ?2 to combine
  different observations/telescopes and obtain
  optimal image
• many measures of entropy available
• replace log with cosh ? emptiness (does not
  enforce positivity)
• less robust and harder to drive than CLEAN
• works well on smooth, extended emission
• trouble with point source sidelobes
• no noise estimate possible from image

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Maximum Entropy
MAXEN model
TD(x,y)
restored image
residual map
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Example Dust around Vega

• tune resolution and sensitivity to suit science
• Wilner et al. 2002, ApJ, 569, L115

Vega 850 ?m Holland et al. 1998
fwhm
2.8 x 2.1 arcsec stellar photosphere
5.3x4.6 arcsec and dust blobs
SCUBA 14 arcsec
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Missing Low Spatial Frequencies (I)

• Large Single Telescope
• make an image by scanning across the sky
• all Fourier components from 0 to D sampled, where
  D is the telescope diameter (weighting depends on
  illumination)
• Fourier transform single dish map T(x,y) ?
  A(x,y), then divide by a(x,y) FTA(x,y), to
  estimate V(u,v)
• choose D large enough to overlap interferometer
  samples of V(u,v) and avoid using data where
  a(x,y) becomes small

density of uv points
(u,v)
53
Missing Low Spatial Frequencies (II)

• separate array of smaller telescopes
• use smaller telescopes observe short baselines
  not accessible to larger telescopes
• shortest baselines from larger telescopes total
  power maps

ALMA with ACA 64 x 12 m 12 to 14000 m 12
x 7 m fills 7 to 12 4 x 12 m fills 0
to 7
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Missing Low Spatial Frequencies (III)

• mosaic with a homogeneous array
• recover a range of spatial frequencies around the
  nominal baseline b using knowledge of A(x,y)
  (Ekers and Rots 1979) (and get shortest baselines
  from total power maps)
• V(u,v) is linear combination of baselines from
  b-D to bD
• depends on pointing direction (xo,yo) as well as
  (u,v)
• Fourier transform with respect to pointing
  direction (xo,yo)

(u,v)
55
Self Calibration

• a priori calibration not perfect
• interpolated from different time, different sky
  direction from source
• basic idea of self calibration
• correct for antenna-based errors together with
  imaging
• works because
• at each time, measure N complex and N(N-1)/2
  visibilities
• source structure represented by small number of
  parameters
• highly overconstrained problem if N large and
  source simple
• in practice, an iterative, non-linear relaxation
  process
• assume initial model ? solve for time dependent
  gains ? form new sky model from corrected data
  using e.g. CLEAN ? solve for new gains
• requires sufficient signal-to-noise ratio for
  each solution interval
• loses absolute position information
• dangerous with small N, complex source, low
  signal-to-noise

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Summary

• Calibrated Visibilities
• ? Fourier Transform
• Dirty Beam and Dirty Image
• ? Deconvolution
• Clean Beam, Sky Model, restored Image
• ? Analysis
• Physical Information on Source

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Concluding Remarks

• interferometry samples visibilities that are
  related to a sky brightness image by the Fourier
  transform
• deconvolution corrects for incomplete sampling
• remember there are usually an infinite number of
  images compatible with the sampled visibilities
• astronomer must use judgement in imaging process
• imaging is fun (compared to calibration)
• many issues not covered today (see References)